本文選題：機(jī)動目標(biāo)跟蹤 + 高階多模型算法　； 參考：《哈爾濱工業(yè)大學(xué)》2017年碩士論文

【摘要】：在機(jī)動目標(biāo)跟蹤領(lǐng)域,由于無法獲知目標(biāo)每一時刻真實(shí)的運(yùn)動情況以及何時發(fā)生了運(yùn)動模型變化,模型的不確定問題就成為目標(biāo)高精度跟蹤的一大核心問題。目前用多模型方法來對機(jī)動目標(biāo)的狀態(tài)進(jìn)行估計(jì)是處理模型不確定性問題最常用有效的方法,其中最經(jīng)典的方法是交互式多模型算法。為了利用更多的先驗(yàn)信息,提高多模型算法的性能,本文研究了高階多模型算法,主要圍繞以下三個方面具體展開。第一:研究了基于混合轉(zhuǎn)移分布的高階交互式多模型濾波算法。與交互式多模型算法相比,高階交互式多模型濾波算法利用更多連續(xù)多個時刻的信息,提高了估計(jì)精度;但是,高階馬爾科夫鏈所需設(shè)置的參數(shù)過多,缺少足夠多的先驗(yàn)知識用以確定高階模型轉(zhuǎn)移概率矩陣。本文采用混合轉(zhuǎn)移分布模型,用一階馬爾可夫模型轉(zhuǎn)移概率的經(jīng)驗(yàn)加權(quán)近似高階模型序列的轉(zhuǎn)移概率,大大減少了所需設(shè)置參數(shù)的個數(shù),降低了合理確定高階模型轉(zhuǎn)移概率矩陣的難度。仿真驗(yàn)證了本算法的有效性;并且階數(shù)越高,在模型不變區(qū)域的估計(jì)性能越好。第二:提出了模型切換受限的高階多模型濾波算法。高階馬爾科夫鏈隱含了每一時刻都可能發(fā)生模型切換的假設(shè),但在實(shí)際場景中這一假設(shè)不合理,目標(biāo)通常不會在所有時刻都發(fā)生運(yùn)動模型切換,為此本文在馬爾可夫鏈基礎(chǔ)上,增加模型切換次數(shù)有限的約束,即連續(xù)多個時間內(nèi)最多只發(fā)生一次模型切換,從而給出了模型序列的更準(zhǔn)確描述,帶來估計(jì)精度的提高;同時由于刪除了很多不符合條件的模型序列,也使得算法計(jì)算效率得到一定改善。另外,配合模型切換受限的多模型濾波,本文還設(shè)計(jì)了一種更為合理的高階模型序列轉(zhuǎn)移概率。推導(dǎo)了模型切換受限的高階廣義偽貝葉斯算法和高階交互式多模型算法。仿真結(jié)果表明:該算法在模型不變區(qū)域的估計(jì)精度與模型序列已知的濾波算法極其接近,僅在模型跳變點(diǎn)處存在尖峰誤差;與交互式多模型算法相比,所有區(qū)域的估計(jì)精度都得到提高;與同階的普通高階多模型濾波算法相比,模型跳變區(qū)域誤差大大降低,過渡過程大為縮短,且節(jié)省了大量的計(jì)算量。第三:提出了模型切換受限的增廣狀態(tài)高階交互式多模型平滑算法。該算法是在模型切換受限的高階多模型濾波算法的基礎(chǔ)上,進(jìn)行狀態(tài)增廣,從而在濾波的同時實(shí)現(xiàn)平滑。仿真實(shí)驗(yàn)表明:該算法與增廣狀態(tài)的交互式多模型平滑算法相比,平滑效果得到進(jìn)一步的提高;與模型切換受限的高階多模型濾波算法相比,估計(jì)精度更好,且基本消除了尖峰誤差;另外該算法可通過設(shè)置不同的固定延遲長度,實(shí)現(xiàn)不同程度的平滑效果。
[Abstract]:In the field of maneuvering target tracking, because it is impossible to know the real movement of the target at every moment and when the moving model changes, the uncertainty of the model becomes a core problem of target tracking with high accuracy. At present, the state estimation of maneuvering targets using multi-model method is the most commonly used and effective method to deal with the uncertainty of the model, among which the most classical one is interactive multi-model algorithm. In order to use more prior information and improve the performance of multi-model algorithm, this paper studies high-order multi-model algorithm, mainly focusing on the following three aspects. First, a high-order interactive multi-model filtering algorithm based on mixed transfer distribution is studied. Compared with the interactive multi-model algorithm, the high-order interactive multi-model filtering algorithm makes use of more information of continuous and multiple times, and improves the estimation accuracy. However, the high-order Markov chain requires too many parameters. There is a lack of enough prior knowledge to determine the transition probability matrix of higher order models. In this paper, the mixed transfer distribution model is used to approximate the transition probability of the high order model sequence with the empirical weighting of the transition probability of the first order Markov model, which greatly reduces the number of required parameters. The difficulty of reasonably determining the transition probability matrix of higher order model is reduced. The simulation results show that the algorithm is effective, and the higher the order is, the better the estimation performance is in the invariant region of the model. Second, a high-order multi-model filtering algorithm with model switching constraints is proposed. The higher-order Markov chain implies the assumption that model switching may occur at every moment, but this assumption is unreasonable in the actual scenario, and the target does not normally have a moving model switch at all times. Therefore, this paper based on Markov chain. By increasing the constraint of the limited number of model switching, that is, the model switching occurs only once at most in a continuous multiple time, thus the more accurate description of the model sequence is given, and the estimation accuracy is improved. At the same time, the computational efficiency of the algorithm is improved due to the deletion of many unqualified model sequences. In addition, a more reasonable transition probability of higher order model sequences is also designed with multi-model filtering with constrained model switching. High-order generalized pseudo-Bayes algorithm and high-order interactive multi-model algorithm are derived. The simulation results show that the estimation accuracy of the algorithm in the invariant region of the model is very close to that of the filter algorithm known by the model sequence, and the peak error exists only at the point of the model jump, and compared with the interactive multi-model algorithm, the proposed algorithm is more accurate than the interactive multi-model algorithm. Compared with the ordinary high-order multi-model filtering algorithm of the same order, the error of the model jump region is greatly reduced, the transition process is greatly shortened, and a large amount of computation is saved. Third, an augmented state high order interactive multi-model smoothing algorithm with constrained model switching is proposed. This algorithm is based on the high-order multi-model filtering algorithm which is limited by model switching, so that the filtering can be smoothed at the same time. The simulation results show that compared with the extended interactive multi-model smoothing algorithm, the proposed algorithm is more effective than the high-order multi-model filtering algorithm with limited model switching, and the estimation accuracy is better than that of the high-order multi-model filtering algorithm with limited model switching. In addition, the algorithm can achieve different degree of smoothing effect by setting different fixed delay length.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TN713

【參考文獻(xiàn)】

相關(guān)期刊論文 前8條

1 吳志遠(yuǎn);毛洋;周敬烈;;基于變維交互式多模型算法的近區(qū)目標(biāo)跟蹤研究[J];電子測量技術(shù);2017年01期

2 劉政;馬增強(qiáng);柳曉云;閆德立;;基于模糊推理的改進(jìn)的交互式多模型算法[J];濟(jì)南大學(xué)學(xué)報(bào)(自然科學(xué)版);2017年02期

3 周彥;胡嵐;王冬麗;;基于IMM-UKF-CS的混合環(huán)境目標(biāo)跟蹤[J];系統(tǒng)仿真學(xué)報(bào);2017年01期

4 高磊;;多模型平滑算法及其在機(jī)動目標(biāo)跟蹤中的應(yīng)用[J];計(jì)算機(jī)仿真;2015年02期

5 毛云祥;牛朝陽;張進(jìn);;基于變結(jié)構(gòu)多模型算法的機(jī)動目標(biāo)無源跟蹤[J];現(xiàn)代防御技術(shù);2014年02期

6 封普文;黃長強(qiáng);曹林平;雍肖駒;;馬爾可夫矩陣修正IMM跟蹤算法[J];系統(tǒng)工程與電子技術(shù);2013年11期

7 蔡艷輝;程鵬飛;李夕銀;;用卡爾曼濾波進(jìn)行GPS動態(tài)定位[J];測繪通報(bào);2006年07期

8 白松浩;空中交通管制監(jiān)視技術(shù)綜述[J];無線電工程;2005年10期

相關(guān)碩士學(xué)位論文 前3條

1 張文杰;基于變結(jié)構(gòu)多模型的機(jī)動目標(biāo)跟蹤方法研究[D];貴州大學(xué);2015年

2 楊洋;基于隱馬爾可夫模型的交互式多模型跟蹤算法優(yōu)化研究[D];華東理工大學(xué);2015年

3 孫粲;機(jī)動目標(biāo)跟蹤交互式多模型算法研究[D];山東大學(xué);2013年

，

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